The field of the invention is systems and methods for reconstructing medical images. More particularly, the invention relates to systems and methods for reconstructing medical images using an image reconstruction framework that accounts for statistical noise so as to increase the attainable signal-to-noise ratio in the reconstructed images.
In recent years, statistical imaging reconstruction has been widely introduced by different CT manufacturers into clinics as a vehicle to reduce radiation dose levels. In these methods, an objective function with a statistically weighted data fidelity term and an often highly nonlinear regularization term is minimized to search for a highest quality CT image with lowest noise level to enhance contrast-to-noise ratio to achieve low dose CT imaging. However, a bottleneck to widely use these developed tools for clinical utility is fundamentally impeded by the slow reconstruction speed, often at the order of hours, for reconstruction of a clinical image volume. This is primarily due to the tradeoff between convergence speed and parallelizability of the used optimization techniques. An optimization technique with high convergence speed often has low parallelizability and vice versa.
It would therefore be desirable to provide systems and methods for reconstructing medical images, in which high convergence speeds can be achieved with high parallelizability while reconstructing images with the benefits of statistical reconstruction techniques.